Identification of Cutting Chatter through Deep Learning and Classification

The traditional analytical method has difficulty in accurately modelling cutting chatter. This paper constructs the vibration datasets of different chatter states and establishes a machine learning (ML) model for chatter identification, treating physical vibration signal as the input. Specifically, the cutting vibration signal was converted into the time-frequency spectrum, which was then classified by a self-designed deep residual convolutional neural network (DR-CNN). After that, the cutting vibration signal was broken down into chatter bands through variational mode decomposition (VMD). The information entropies of the chatter bands were calculated as cutting chatter features. Next, support vector machine (SVM) was introduced to classify the extracted features and used to create an online cutting chatter identification algorithm. The proposed method achieved a much higher mean identification accuracy (92.57 %) than the traditional identification method.

Chatter identification in thin-wall milling using an adaptive variational mode decomposition method combined with the decision tree model

Chatter is prone to occur in thin-wall part milling process due to the low stiffness and damping of the workpiece. It roughens the machining surface, shortens the tool life, and thus should be detected and prevented. However, the multimode and time-varying dynamics of thin-wall parts produces nonstationary and multicomponent cutting signals, which makes it challenging to accurately identify the chatter occurrence. In this article, an effective chatter identification method based on adaptive variational mode decomposition and decision tree is presented to tackle this problem. The adaptive variational mode decomposition is used to adaptively decompose the raw acoustic signal into several subsignals, and the decision tree is used to automatically determine the chatter threshold. First, the convergence of multicenter-frequency signal processing is analyzed and is proved to be closely related to the accuracy of variational mode decomposition. Afterward, a criterion is set up to initialize the center frequencies of variational mode decomposition, on the basis of which an adaptive energy ratio-based method with good computational efficiency is presented to extract the frequencies of the main components from the raw signal. The initial center frequencies and the number of modes of variational mode decomposition are simultaneously obtained. As a result, the raw signal is adaptively decomposed into several subsignals, which contain its principal components. Then, the normalized energy and sample entropy of the subsignals are selected to establish the decision tree model for automatic chatter identification. Several milling tests on a thin-wall plate are carried out to verify the proposed method. The results show that chatter can be identified accurately and efficiently using the proposed method.